First, write a code chunk to check, install and load the following R packages:
plotly, R library for plotting interactive statistical graphs.
gganimate, an ggplot extension for creating animated statistical graphs.
gifski converts video frames to GIF animations using pngquant’s fancy features for efficient cross-frame palettes and temporal dithering. It produces animated GIFs that use thousands of colors per frame.
gapminder: An excerpt of the data available at Gapminder.org. We just want to use its country_colors scheme.
tidyverse, a family of modern R packages specially designed to support data science, analysis and communication task including creating static statistical graphs.
The downloaded binary packages are in
/var/folders/zh/67j59v916z17skl95tl1wmzw0000gn/T//RtmpkAMbmV/downloaded_packages
gifski installed
The downloaded binary packages are in
/var/folders/zh/67j59v916z17skl95tl1wmzw0000gn/T//RtmpkAMbmV/downloaded_packages
gapminder installed
also installing the dependencies 'proxy', 'e1071', 'wk', 'classInt', 's2', 'units', 'sf', 'lpSolve', 'transformr', 'tweenr'
The downloaded binary packages are in
/var/folders/zh/67j59v916z17skl95tl1wmzw0000gn/T//RtmpkAMbmV/downloaded_packages
gganimate installed
1.2 Importing the data
In this hands-on exercise, the Data worksheet from GlobalPopulation Excel workbook will be used.
Write a code chunk to import Data worksheet from GlobalPopulation Excel workbook by using appropriate R package from tidyverse family.
col <-c("Country", "Continent")globalPop <-read_xls("data/GlobalPopulation.xls",sheet="Data") %>%mutate_each_(funs(factor(.)), col) %>%mutate(Year =as.integer(Year))
Warning: `mutate_each_()` was deprecated in dplyr 0.7.0.
ℹ Please use `across()` instead.
Warning: `funs()` was deprecated in dplyr 0.8.0.
ℹ Please use a list of either functions or lambdas:
# Simple named list: list(mean = mean, median = median)
# Auto named with `tibble::lst()`: tibble::lst(mean, median)
# Using lambdas list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
NoteThings to learn fro the code chunk above
read_xls() of readxl package is used to import the Excel worksheet.
mutate_each_() of dplyr package is used to convert all character data type into factor.
mutate of dplyr package is used to convert data values of Year field into integer.
Unfortunately, mutate_each_() was deprecated in dplyr 0.7.0. and funs() was deprecated in dplyr 0.8.0. In view of this, we will re-write the code by using mutate_at() as shown in the code chunk below.
col <-c("Country", "Continent")globalPop <-read_xls("data/GlobalPopulation.xls",sheet="Data") %>%mutate_at(col, as.factor) %>%mutate(Year =as.integer(Year))
Instead of using mutate_at(), across() can be used to derive the same outputs.
col <-c("Country", "Continent")globalPop <-read_xls("data/GlobalPopulation.xls",sheet="Data") %>%mutate(across(col, as.factor)) %>%mutate(Year =as.integer(Year))
2. Animated Data Visualisation: gganimate methods
gganimate extends the grammar of graphics as implemented by ggplot2 to include the description of animation. It does this by providing a range of new grammar classes that can be added to the plot object in order to customise how it should change with time.
transition_*() defines how the data should be spread out and how it relates to itself across time.
view_*() defines how the positional scales should change along the animation.
shadow_*() defines how data from other points in time should be presented in the given point in time.
enter_*()/exit_*() defines how new data should appear and how old data should disappear during the course of the animation.
ease_aes() defines how different aesthetics should be eased during transitions.
2.1 Building a static population bubble plot
In the code chunk below, the basic ggplot2 functions are used to create a static bubble plot.
transition_time() of gganimate is used to create transition through distinct states in time (i.e. Year).
ease_aes() is used to control easing of aesthetics. The default is linear. Other methods are: quadratic, cubic, quartic, quintic, sine, circular, exponential, elastic, back, and bounce.
In Plotly R package, both ggplotly() and plot_ly() support key frame animations through the frame argument/aesthetic. They also support an ids argument/aesthetic to ensure smooth transitions between objects with the same id (which helps facilitate object constancy).
3.1 Building an animated bubble plot: ggplotly()
We will create an animated bubble plot by using ggplotly() method.
Warning in p$x$data[firstFrame] <- p$x$frames[[1]]$data: number of items to
replace is not a multiple of replacement length
The animated bubble plot above includes a play/pause button and a slider component for controlling the animation.
Notice that although show.legend = FALSE argument was used, the legend still appears on the plot. To overcome this problem, theme(legend.position='none') should be used as shown in the plot and code chunk below.
bp <- globalPop %>%plot_ly(x =~Old, y =~Young, size =~Population, color =~Continent,sizes =c(2, 100),frame =~Year, text =~Country, hoverinfo ="text",type ='scatter',mode ='markers' ) %>%layout(showlegend =FALSE)bp
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